code: def encode(self, column_data):
      transformed_data = self.tfidf_vectorizer.transform(
          [str(x) for x in column_data]
      )
      dense_transformed_data = [
          np.array(x.todense())[0] for x in transformed_data
      ]
      return torch.Tensor(dense_transformed_data)
  
file_path: lightwood/lightwood/encoder/text/tfidf.py
imports: import torch
  from sklearn.feature_extraction.text import TfidfVectorizer
  import numpy
  from lightwood.encoder.base import BaseEncoder
label_desc: Use image node_id 3 for calling the TfidfEncoder obj's underlying member method code with example usage: obj.encode(column_data) and returns: torch